{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2f6feff6-0037-48f3-8bd5-bd2d4afacf65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in d:\\programdata\\anaconda3\\lib\\site-packages (1.26.4)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "raw_df=np.loadtxt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "682bcd98-83ce-4d30-ada7-3b73077afba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)\n",
    "ac=accuracy_score(y_test,model.predict(x_test))\n",
    "print(\"模型预测准确率: \",ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1ba6b9fa-0af2-4717-bd10-1549dd6749c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np\n",
    "N,M=500,500\n",
    "t1=np.linspace(0,100,N)\n",
    "t2=np.linspace(0,100,M)\n",
    "x1,x2=np.meshgrid(t1,t2)\n",
    "x_new=np.stack((x1.flat,x2.flat),axis=1)\n",
    "y_predict=model.predict(x_new)\n",
    "y_hat=y_predict.reshape(x1.shape)\n",
    "iris_cmap=ListedColormap([\"#ACF080\",\"#A0A0FF\"])\n",
    "plt.pcolormesh(x1,x2,y_hat,cmap=iris_cmap)\n",
    "plt.scatter(x[y==0,0],x[y==0,1],s=30,c='b',marker='o')\n",
    "plt.scatter(x[y==1,0],x[y==1,1],s=30,c='r',marker='^')\n",
    "plt.rcParams['font.sans-serif'] = 'Simhei'\n",
    "plt.xlabel('科目1成绩')\n",
    "plt.ylabel('科目2成绩')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c2240b3-988c-4ce8-97ff-55de1f9a149c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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